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   https://doi.org/10.1016/j.epsr.2022.108543  

   Chen, Sunny ; Mathieu, Johanna L. ; Seiler, Peter ( October 2022 , Electric Power Systems Research)
2. A dynamic model of wind turbine yaw for active farm control

   https://doi.org/10.1002/we.2884  

   Starke, Genevieve M. ; Meneveau, Charles ; King, Jennifer R. ; Gayme, Dennice F. ( December 2023 , Wind Energy)

   This paper presents a graph‐based dynamic yaw model to predict the dynamic response of the hub‐height velocities and the power of a wind farm to a change in yaw. The model builds on previous work where the turbines define the nodes of the graph and the edges represent the interactions between turbines. Advances associated with the dynamic yaw model include a novel analytical description of the deformation of wind turbine wakes under yaw to represent the velocity deficits and a more accurate representation of the interturbine travel time of wakes. The accuracy of the model is improved by coupling it with time‐ and space‐dependent estimates of the wind farm inflow based on real‐time data from the wind farm. The model is validated both statically and dynamically using large‐eddy simulations. An application of the model is presented that incorporates the model into an optimal control loop to control the farm power output.

    

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3. LiDAR measurements for an onshore wind farm: Wake variability for different incoming wind speeds and atmospheric stability regimes

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4. A Wake Modeling Paradigm for Wind Farm Design and Control

   https://doi.org/10.3390/en12152956  

   Shapiro, Carl R. ; Starke, Genevieve M. ; Meneveau, Charles ; Gayme, Dennice F. ( August 2019 , Energies)

   null (Ed.)

   Wake models play an integral role in wind farm layout optimization and operations where associated design and control decisions are only as good as the underlying wake model upon which they are based. However, the desired model fidelity must be counterbalanced by the need for simplicity and computational efficiency. As a result, efficient engineering models that accurately capture the relevant physics—such as wake expansion and wake interactions for design problems and wake advection and turbulent fluctuations for control problems—are needed to advance the field of wind farm optimization. In this paper, we discuss a computationally-efficient continuous-time one-dimensional dynamic wake model that includes several features derived from fundamental physics, making it less ad-hoc than prevailing approaches. We first apply the steady-state solution of the model to predict the wake expansion coefficients commonly used in design problems. We demonstrate that more realistic results can be attained by linking the wake expansion rate to a top-down model of the atmospheric boundary layer, using a super-Gaussian wake profile that smoothly transitions between a top-hat and Gaussian distribution as well as linearly-superposing wake interactions. We then apply the dynamic model to predict trajectories of wind farm power output during start-up and highlight the improved accuracy of non-linear advection over linear advection. Finally, we apply the dynamic model to the control-oriented application of predicting power output of an irregularly-arranged farm during continuous operation. In this application, model fidelity is improved through state and parameter estimation accounting for spanwise inflow inhomogeneities and turbulent fluctuations. The proposed approach thus provides a modeling paradigm with the flexibility to enable designers to trade-off between accuracy and computational speed for a wide range of wind farm design and control applications. 

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5. An approach for wide-area damping control using multiple DFIG-based wind farm to deal with communication dropouts

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   Yogarathinam, Amirthagunaraj ; Chaudhuri, Nilanjan Ray ( December 2016 , Innovative Smart Grid Technologies Conference (ISGT), 2016 IEEE Power & Energy Society)